P1744: ACCURACY OF PREDICTING LONG-TERM SURVIVAL OF CHIMERIC ANTIGEN RECEPTOR (CAR) T CELL THERAPIES IN LARGE B-CELL LYMPHOMA (LBCL)
Authors: E. J. Verburg-Baltussen, A. Stewart, F. F. Liu, C. Parker, E. F. Peterse, M. Treur, B. Malcolm, S. Klijn
Journal: HemaSphere (2022)

Background: In health economic evaluations of CAR T cell therapies in LBCL, long-term survival predictions have been based on pivotal overall survival (OS) study data. Due to several reasons including the immaturity of these studies, various survival models have been used to extrapolate OS. Selection of the survival model may be guided by statistical goodness-of-fit criteria (GOFC), which provide a relative measure of fit to the observed data. However, GOFC are not necessarily indicative of predictive accuracy beyond the study follow-up. Aims: The primary objective was to retrospectively examine the predictive accuracy of different survival models for estimating OS of CAR T cell therapies in LBCL using data cutoffs with different follow-up durations from the respective studies. The secondary objective was to assess the appropriateness of using GOFC as guidance for survival model selection. Methods: OS data for tisagenlecleucel, axicabtagene ciloleucel, and lisocabtagene maraleucel were sourced from JULIET (NCT02445248), ZUMA-1 (NCT02348216), and TRANSCEND NHL 001 (NCT02631044), respectively. A broad range of survival models (standard parametric, cubic spline, mixture cure, and mixture models) were fit to multiple data cutoffs, with varying follow-up durations for each study. Goodness-of-fit was assessed using the Akaike Information Criterion and Bayesian Information Criterion. Predictive accuracy of each model was calculated as the mean absolute error (MAE) relative to OS observed in the most mature data cutoff. The correlation between GOFC and predictive accuracy was assessed using linear regression. Results: The importance of selecting an appropriate survival model was demonstrated by the large variations seen in the 5-year survival estimates of the different extrapolation models, which ranged between 0%–45%, 10%–55%, and 0%–47% over all data cutoffs of JULIET, ZUMA1, and TRANSCEND NHL 001, respectively. For all studies, cubic spline and mixture cure models provided the best predictive accuracy for the least mature data cutoffs (MAE, 0.014–0.128 and 0.013–0.085, respectively), meaning that those models were closest to the observed data of the most mature data cutoff. The predictive accuracy of the standard parametric and mixture models showed larger variations (MAE, 0.024–0.162 and 0.013–0.176, respectively). With increasing maturity of the data, cubic spline and mixture cure models continued to have high predictive accuracy (MAE, 0.014–0.036 and 0.015–0.050, respectively). Mixture models showed an increase in predictive accuracy when longer follow-up data was available. Standard parametric models provided poor predictive accuracy, even with longer follow-up. Furthermore, for the least mature data, the correlation between GOFC and predictive accuracy was low, demonstrated by a coefficient of determination < 20%, for all studies. With increasing data maturity, an increase in correlation between GOFC and predictive accuracy was observed. Summary/Conclusion: Based on the data currently available, cubic spline and mixture cure models can achieve higher predictive accuracy for OS in LBCL compared with standard parametric and mixture models, even when follow-up is short. As such, these methods should be considered when modeling OS in LBCL. GOFC demonstrated a low correlation with predictive accuracy; thus, limited weight should be assigned to GOFC when selecting the optimal survival model in LBCL.